The hammam effect or how a warm ocean enhances large scale atmospheric predictability

Local rainfall forecast knowledge across the globe used for agricultural decision-making

The agriculture sector is vital to the world's economy and weather and climate are key drivers that affect the productivity and profitability of agricultural systems. At the same time, weather-related risks pose significant challenges to farmers' livelihoods. Although scientific weather forecast (SFK) is available, many farmers, especially in the Global South, have limited access to this information, and they rely on local forecast knowledge (LFK) to make farming decisions. Many studies also recognize the value of combining both forecasting systems; yet, unlike SFK which is readily available, indicators for LFK needs to be collected first. Therefore, this study identifies and documents the spatial distribution of LFK use for agriculture across the globe through a systematic literature review. Results show that a high number of LFK regions with a total of around 1350 local environmental indicators were found in Africa and Asia and less in South and North America. The low usability of scientific weather forecasts is perceived as the main reason farmers use LFK instead of SFK, yet the accessibility of LFK both for scientists and users, needs to be improved. Indicators based on animals and meteorology appeared to be more frequently used for weather predictions than plant- and astronomy-based indicators. Digitalizing the LFK inventory and collecting more detailed information about the regions where LFK was identified could promote and foster research on integrating scientific and local forecasting systems. This study will draw attention to the importance of LFK in weather forecasting, maintain this knowledge and enhance it.

Statistical performance of local attractor dimension estimators in non-Axiom A dynamical systems

We investigate various estimators based on extreme value theory (EVT) for determining the local fractal dimension of chaotic dynamical systems. In the limit of an infinitely long time series of an ergodic system, the average of the local fractal dimension is the system's global attractor dimension. The latter is an important quantity that relates to the number of effective degrees of freedom of the underlying dynamical system, and its estimation has been a central topic in the dynamical systems literature since the 1980s. In this work, we propose a framework that combines phase space recurrence analysis with EVT to estimate the local fractal dimension around a particular state of interest. While the EVT framework allows for the analysis of high-dimensional complex systems, such as the Earth's climate, its effectiveness depends on robust statistical parameter estimation for the assumed extreme value distribution. In this study, we conduct a critical review of several EVT-based local fractal dimension estimators, analyzing and comparing their performance across a range of systems. Our results offer valuable insights for researchers employing the EVT-based estimates of the local fractal dimension, aiding in the selection of an appropriate estimator for their specific applications.

Dynamical diagnostic of extreme events in Venice lagoon and their mitigation with the MoSE

Extreme events are becoming more frequent due to anthropogenic climate change, posing serious concerns on societal and economic impacts and asking for mitigating strategies, as for Venice. Here we proposed a dynamical diagnostic of Extreme Sea Level (ESL) events in the Venice lagoon by using two indicators based on combining extreme value theory and dynamical systems: the instantaneous dimension and the inverse persistence. We show that the latter allows us to localize ESL events with respect to sea level fluctuations around the astronomical tide, while the former informs us on the role of active processes across the lagoon and specifically on the constructive interference of atmospheric contributions with the astronomical tide. We further examined the capability of the MoSE (Experimental Electromechanical Module), a safeguarding system recently put into operation, in mitigating extreme flooding events in relation with the values of the two dynamical indicators. We show that the MoSE acts on the inverse persistence in reducing/controlling the amplitude of sea level fluctuation and provide a valuable support for mitigating ESL events if operating, in a full operational mode, at least several hours before the occurrence an event.

Dynamical footprints of hurricanes in the tropical dynamics

Hurricanes-and more broadly tropical cyclones-are high-impact weather phenomena whose adverse socio-economic and ecosystem impacts affect a considerable part of the global population. Despite our reasonably robust meteorological understanding of tropical cyclones, we still face outstanding challenges for their numerical simulations. Consequently, future changes in the frequency of occurrence and intensity of tropical cyclones are still debated. Here, we diagnose possible reasons for the poor representation of tropical cyclones in numerical models, by considering the cyclones as chaotic dynamical systems. We follow 197 tropical cyclones which occurred between 2010 and 2020 in the North Atlantic using the HURDAT2 and ERA5 data sets. We measure the cyclones instantaneous number of active degrees of freedom (local dimension) and the persistence of their sea-level pressure and potential vorticity fields. During the most intense phases of the cyclones, and specifically when cyclones reach hurricane strength, there is a collapse of degrees of freedom and an increase in persistence. The large dependence of hurricanes dynamical characteristics on intensity suggests the need for adaptive parametrization schemes which take into account the dependence of the cyclone's phase, in analogy with high-dissipation intermittent events in turbulent flows.

Future intensification of extreme Aleutian low events and their climate impacts

Extreme Aleutian Low (AL) events have been associated with major ecosystem reorganisations and unusual weather patterns in the Pacific region, with serious socio-economic consequences. Yet, their future evolution and impacts on atmosphere-ocean interactions remain uncertain. Here, a large ensemble of historical and future runs from the Community Earth System Model is used to investigate the evolution of AL extremes. The frequency and persistence of AL extremes are quantified and their connection with climatic variables is examined. AL extremes become more frequent and persistent under the RCP8.5 scenario, associated with changes in precipitation and air temperature patterns over North America. Future changes in AL extremes also increase the variability of the sea surface temperature and net heat fluxes in the Kuroshio Extension, the most significant heat and energy flux region of the basin. The increased frequency and persistence of future AL extremes may potentially cause substantial changes in fisheries and ecosystems of the entire Pacific region as a knock-on effect.